Advanced Search
MyIDEAS: Login to save this article or follow this journal

Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms

Contents:

Author Info

  • Roberts, G. O.
  • Smith, A. F. M.
Registered author(s):

    Abstract

    Markov chain Monte Carlo (MCMC) simulation methods are being used increasingly in statistical computation to explore and estimate features of likelihood surfaces and Bayesian posterior distributions. This paper presents simple conditions which ensure the convergence of two widely used versions of MCMC, the Gibbs sampler and Metropolis-Hastings algorithms.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.sciencedirect.com/science/article/B6V1B-45FCSJC-3N/2/aab5948f80ed0f595662fa9e182fb9af
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 49 (1994)
    Issue (Month): 2 (February)
    Pages: 207-216

    as in new window
    Handle: RePEc:eee:spapps:v:49:y:1994:i:2:p:207-216

    Contact details of provider:
    Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description

    Order Information:
    Postal: http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
    Web: https://shop.elsevier.com/OOC/InitController?id=505572&ref=505572_01_ooc_1&version=01

    Related research

    Keywords: Markov chain Monte Carlo Gibbs sampler Metropolis-Hastings algorithm statistical computation ergodicity lower semicontinuity;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:spapps:v:49:y:1994:i:2:p:207-216. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.